We perform this set of experiments on two publicly available datasets: The MVTecAD dataset [1]} for industrial anomaly detection, and the AR Face dataset [2]} for facial occlusion detection.
The MVTecAD dataset consists of \(\sim \)5300 images of 15 categories of object classes, with \(\sim \)4100 non-anomalous samples and \(\sim \)1200 images with multiple types of anomalies.
The AR Face dataset consists of 2600 frontal images of faces with varying illumination and facial expressions, with 1200 of them containing occlusions caused by sunglasses and scarf.
Images from both datasets are resized and converted to 128\(\times \) 128 grayscale.
Examples from these datasets can be seen in Figure REF  (first rows).
